The current competitive market condition in the oil industry is so concentrated on companies' budgets that they require methods to extract oil from wells at the lowest possible cost. All pumping units, new or old, require regular preventive maintenance and constant inspection, and with 30 percent of total oil production coming from sucker rod pumps, the cost of diagnostics must be reduced accordingly.This article focuses on the intelligent diagnostics of the rod and borehole pump for preventive maintenance and monitoring during the life cycle of the well. The use of artificial intelligence methods for predictive diagnostics of equipment condition solves problems without production stoppages and without additional interventions from outside.

As the volume of data continues to surge, researchers are confronted with the challenge of extracting meaningful insights from this wealth of information. Despite rapid advancements in Natural Language Processing (NLP) techniques in the AI industry, there remain gaps and opportunities for further exploration, particularly in the realm of data recombination techniques and methods. This paper proposes a novel text recombination method to facilitate the generation of recombined words from a given text. The process commences with a ’stanza’ model, which identifies and compiles Named Entity Recognitions (NERs) into a list. These NERs are then cross-referenced with Wikipedia pages to retrieve relevant information, thereby enhancing entity understanding and analysis. The ensuing step involves preprocessing the output text from the previous stage, generating a list of unique words while eliminating stop words. This preprocessing stage serves to remove noise and focus on meaningful words, laying the groundwork for more effective clustering. To enable clustering, we employ vector embeddings, representing words in a 2-dimensional space, rendering them suitable for clustering techniques. Notably, the proposed method further enhances results by re-clustering words after applying K-Means, thereby identifying the most fitting candidate words for recombination. Comparatively, this method outperforms large language models (LLMs) due to its incorporation of NER information, utilization of Wikipedia pages, and effective preprocessing techniques. Unlike LLMs, which operate as resource-intensive black boxes on static data, this method benefits from real-time information access and knowledge base updates. Furthermore, each stage of the process is visualized to control the progress correctly. Thus, due to the plots of word clusterization, the proposed text recombination approach showed positive results.

Accurate event extraction and analysis are vital for understanding trends and making informed decisions in various domains. This paper presents a concise rule-based approach for event extraction in the aerospace domain, aiming to enhance decision-making processes. The proposed methodology utilizes rule-based pattern-matching techniques to identify and extract events from textual data, such as news articles and research papers. By leveraging linguistic features and syntactic structures, the approach effectively captures event-related information. Additionally, an event map visualization technique is introduced to provide a comprehensive overview of the extracted events and their relationships. The proposed approach, integrated into the Aerospace Expert System (AES), offers a powerful tool for tracking and analyzing events in the aerospace industry. Evaluation results demonstrate the high precision and recall of the rule-based approach, enabling stakeholders to gain valuable insights and make informed decisions in the aerospace domain.

Green innovation refers to developing and implementing new technologies, practices, products, and processes that promote sustainability and reduce environmental impacts. This article postulates the conceptualisation and implementation of an intelligent decision support system (IDSS) tailored to the aerospace technology sector. The data were collected from open sources such as social media and analyzed using the natural language processing tool. The envisaged IDSS is a comprehensive and seamlessly integrated platform designed to undergird decision-making, problem-solving, and research initiatives within the aerospace industry. Catering to the sector’s engineers, technicians, and managerial cadres, it aims to unravel complex datasets, proffer incisive analyses, and furnish prudent advice and recommendations. Its multifaceted capabilities range from data search and optimisation to modelling and forecasting. With an emphasis on harmonious integration with extant aerospace systems, it strives to provide engineers and technicians with enriched data insights. Moreover, its design ethos is centred on user-friendliness, underscored by an intuitive graphical interface that expedites seamless access and utilisation. Ultimately, the envisioned IDSS will augment the aerospace industry’s analytical prowess and will serve as a potent instrument for effective decision-making. © 2023 by the authors.

This paper proposes a predictive model to help oil workers build a reliable model for identifying oilwell failures. It can help geologists experienced with Machine Learning to improve the accuracy of failure identification and a more accurate approach to well-maintenance planning. This study is based on output data statistics such as per-well daily oil flowmeter readings. The volatility of these indications makes it possible to determine the probability of an oilwell failure. This method makes it possible to rank wells according to the principle of the most probable failures for workers making decisions. The use of predictive diagnostics can help to detect equipment problems early, thereby minimizing unplanned downtime. Unplanned sudden oilwell failures increase the company’s operating costs, as well as increase risks of environmental pollution. © 2024 «OilGasScientificResearchProject» Institute. All rights reserved.

This study explores the use of unsupervised machine learning to optimize operational efficiency and reduce downtime in oil well production through clustering analysis. Using production data from a major Kazakhstani oil operator, the study applies K-means clustering to categorize oil wells based on output patterns. The dataset includes more than three million entries over a two-year period, capturing daily values for oil volume, liquid volume, and water cut percentage. Data preprocessing involved normalization, outlier handling, and correlation analysis to ensure robust clustering performance. The optimal number of clusters (K = 10) was selected using the Elbow method. Cluster interpretations revealed distinct operational profiles, including stable, declining, and volatile well behavior. In particular, more than 70% of wells remained in their assigned clusters over time, suggesting temporal stability and operational consistency. By identifying risk-prone and underperforming wells, the results support proactive maintenance, informed resource allocation, and long-term planning. The study demonstrates how unsupervised learning techniques can support data-driven decision-making in the petroleum industry without reliance on expensive sensor infrastructure. This research provides a replicable framework for oilfield analysis and highlights the value of behavioral clustering as a predictive tool for operational optimization. Our methodology integrates unsupervised learning, dimensionality reduction techniques, and visual analytics to provide interpretable cluster groupings. The enhanced predictive model demonstrates the potential to support data-driven decision-making and resource allocation in oilfield operations. © 2025 «OilGasScientificResearchProject» Institute. All rights reserved.

Углеродные сорбенты широко применяются в системе водоочистки ввиду своих адаптируемых свойств к различного вида загрязнителям. Удаление радионуклидов, в частности, радиоактивного йода представляет собой сложную задачу, поскольку его состав включает анионы и молекулярный йод (I2) в воде и воздухе. В данной работе обсуждаются способность и механизм сорбции йода углеродными сорбентами на основе биомассы. В данном исследовании впервые была получена серия активированных углей на основе скорлупы грецкого ореха, прошедшей химическую активацию КОН и модифицированной азотными группами за счет гидротермального внедрения мочевины в структуру активированного угля. Легирование азотом проводилось для внедрения положительно заряженных активных центров. Работа включает оценку подготовки, характеристики и эффективности углеродсодержащих сорбентов для удаления соединений йода из водных сред. Разработан простой однореакторный синтез композита WN Urea KOH на основе активированного угля, модифицированного азотсодержащими группами. Сорбционная емкость сорбента составила 459,42 мг I- на грамм композита с начальной концентрацией йодид иона 100 мг/л. Сорбционная емкость композита WN KOH без модификации азотными группами составила 432,5 мг/г с начальной концентрацией йодид иона 100 мг/л.

The article presents the results of computer modeling of the microstructure of a composite material consisting of a polyamide matrix and metal inclusions.The researched composition is one of the advanced trends in the production of materials for advancedMIM technologies. This paper describes the design of a new pressing device to produce composite rods. Computer modeling of mechanical properties of composite metal-polymer material was performed in the high-level CAD system DIGIMAT module MF. Micro-level modeling of the composite made it possible to establish the relationship between the percentage of inclusions and the upper yield strength of the material and the influence of the shape of the inclusions on the tensile strength of the designed material. The computational experiment results were verified with the results of the field experiment for the samples produced by the injection molding technology and the 3D printing technology.

Screw compressors are critical equipment in oil and gas production and transportation, where efficiency losses caused by rotor geometry, inlet pressure pulsations, and harsh climatic conditions can accumulate into substantial annual energy penalties and reliability degradation. This study provides a quantitative assessment of these coupled effects within a unified multiphysics framework that combines time-accurate transient CFD simulations based on a fixed Cartesian immersed-boundary formulation with a climate-calibrated offline physics-based digital twin—functioning as a digital shadow with one-way data flow from archival SCADA records—a reduced-order seasonal model with no real-time updating, calibrated against a full calendar year of SCADA records and validated against a held-out cold-season dataset (October–December 2022, Tamb = −15 to +8 °C); summer-period predictions rely on calibrated extrapolation beyond the validation window—an integration not previously demonstrated for oil-flooded screw compressors. Two rotor profile configurations (Type A and Type B) were analyzed to quantify geometry-driven differences in static pressure distribution, leakage tendency, and pulsation sensitivity. Transient suction conditions were modeled using harmonic and quasi-random inlet pressure disturbances to evaluate pressure amplification, phase lag, leakage intensification, and efficiency degradation. Seasonal performance was assessed by integrating temperature-dependent gas properties, oil viscosity behavior, and external heat transfer into an annual climatic load framework.

The experimental results obtained during the work enrich modern medicinal chemistry with new information about the development and local anesthetic activity of a promising class of chemical compound - O-benzoyloxime of bispidine. The discovered new properties of the molecule can serve as the basis for the creation of a new domestic safe local anesthetic drug that is effective in models of infiltration and conduction anesthesia and has low toxicity. The aim of this work is to synthesize and study the physicochemical and biological properties of a new morpholino-substituted bispidine derivative. Results and discussion. 3-(3-(Ethan-1-yloxy)propyl)-7-[3-(morpholine-4-yl)propyl]-3,7-diazabicyclo[3.3.1]nonan-9-one was constructed in 56.9% yield. Oximation under the influence of a powerful oximylating agent led to the oxime. O-Benzoyloxime was synthesized by benzoylation of the oxime. The complex of O-benzoyloxime with β-cyclodextrin - LA-180 was studied for local anesthetic activity and acute toxicity. Conclusion. It was established that LA-180 manifested itself as a highly active compound in a series of experiments studying infiltration and conduction anesthesia. The results of a study of acute toxicity after a single subcutaneous administration to white outbred mice showed that LA-180 (925 mg/kg) is a low-toxic compound in comparison with standard drugs such as trimecaine, lidocaine and novocaine.